Overview

Dataset statistics

Number of variables18
Number of observations35064
Missing cells8523
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory144.0 B

Variable types

Numeric15
Categorical3

Alerts

station has constant value ""Constant
CO is highly overall correlated with NO2 and 3 other fieldsHigh correlation
DEWP is highly overall correlated with PRES and 1 other fieldsHigh correlation
NO2 is highly overall correlated with CO and 4 other fieldsHigh correlation
No is highly overall correlated with yearHigh correlation
O3 is highly overall correlated with NO2 and 1 other fieldsHigh correlation
PM10 is highly overall correlated with CO and 2 other fieldsHigh correlation
PM2.5 is highly overall correlated with CO and 2 other fieldsHigh correlation
PRES is highly overall correlated with DEWP and 1 other fieldsHigh correlation
SO2 is highly overall correlated with CO and 1 other fieldsHigh correlation
TEMP is highly overall correlated with DEWP and 2 other fieldsHigh correlation
year is highly overall correlated with NoHigh correlation
PM2.5 has 913 (2.6%) missing valuesMissing
PM10 has 548 (1.6%) missing valuesMissing
SO2 has 1296 (3.7%) missing valuesMissing
NO2 has 1365 (3.9%) missing valuesMissing
CO has 2178 (6.2%) missing valuesMissing
O3 has 1489 (4.2%) missing valuesMissing
wd has 483 (1.4%) missing valuesMissing
RAIN is highly skewed (γ1 = 25.13980924)Skewed
No is uniformly distributedUniform
No has unique valuesUnique
hour has 1461 (4.2%) zerosZeros
RAIN has 33702 (96.1%) zerosZeros
WSPM has 792 (2.3%) zerosZeros

Reproduction

Analysis started2024-03-08 05:14:28.770921
Analysis finished2024-03-08 05:15:10.740197
Duration41.97 seconds
Software versionydata-profiling vv4.6.5
Download configurationconfig.json

Variables

No
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct35064
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17532.5
Minimum1
Maximum35064
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:10.858885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1754.15
Q18766.75
median17532.5
Q326298.25
95-th percentile33310.85
Maximum35064
Range35063
Interquartile range (IQR)17531.5

Descriptive statistics

Standard deviation10122.249
Coefficient of variation (CV)0.57734204
Kurtosis-1.2
Mean17532.5
Median Absolute Deviation (MAD)8766
Skewness0
Sum6.1475958 × 108
Variance1.0245993 × 108
MonotonicityStrictly increasing
2024-03-08T12:15:11.036932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
23379 1
 
< 0.1%
23373 1
 
< 0.1%
23374 1
 
< 0.1%
23375 1
 
< 0.1%
23376 1
 
< 0.1%
23377 1
 
< 0.1%
23378 1
 
< 0.1%
23380 1
 
< 0.1%
23422 1
 
< 0.1%
Other values (35054) 35054
> 99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
35064 1
< 0.1%
35063 1
< 0.1%
35062 1
< 0.1%
35061 1
< 0.1%
35060 1
< 0.1%
35059 1
< 0.1%
35058 1
< 0.1%
35057 1
< 0.1%
35056 1
< 0.1%
35055 1
< 0.1%

year
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
2016
8784 
2014
8760 
2015
8760 
2013
7344 
2017
1416 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140256
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2013
2nd row2013
3rd row2013
4th row2013
5th row2013

Common Values

ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Length

2024-03-08T12:15:11.280260image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:15:11.461145image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2016 8784
25.1%
2014 8760
25.0%
2015 8760
25.0%
2013 7344
20.9%
2017 1416
 
4.0%

Most occurring characters

ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140256
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 140256
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35064
25.0%
0 35064
25.0%
1 35064
25.0%
6 8784
 
6.3%
4 8760
 
6.2%
5 8760
 
6.2%
3 7344
 
5.2%
7 1416
 
1.0%

month
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5229295
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:11.728638image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4487524
Coefficient of variation (CV)0.52871219
Kurtosis-1.2080577
Mean6.5229295
Median Absolute Deviation (MAD)3
Skewness-0.0092942217
Sum228720
Variance11.893893
MonotonicityNot monotonic
2024-03-08T12:15:11.933281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
1 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5592
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2712
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%

day
Real number (ℝ)

Distinct31
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.729637
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:12.114109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8002175
Coefficient of variation (CV)0.55946729
Kurtosis-1.1940295
Mean15.729637
Median Absolute Deviation (MAD)8
Skewness0.0067598056
Sum551544
Variance77.443829
MonotonicityNot monotonic
2024-03-08T12:15:12.333773image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1152
 
3.3%
2 1152
 
3.3%
28 1152
 
3.3%
27 1152
 
3.3%
26 1152
 
3.3%
25 1152
 
3.3%
24 1152
 
3.3%
23 1152
 
3.3%
22 1152
 
3.3%
21 1152
 
3.3%
Other values (21) 23544
67.1%
ValueCountFrequency (%)
1 1152
3.3%
2 1152
3.3%
3 1152
3.3%
4 1152
3.3%
5 1152
3.3%
6 1152
3.3%
7 1152
3.3%
8 1152
3.3%
9 1152
3.3%
10 1152
3.3%
ValueCountFrequency (%)
31 672
1.9%
30 1056
3.0%
29 1080
3.1%
28 1152
3.3%
27 1152
3.3%
26 1152
3.3%
25 1152
3.3%
24 1152
3.3%
23 1152
3.3%
22 1152
3.3%

hour
Real number (ℝ)

ZEROS 

Distinct24
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.5
Minimum0
Maximum23
Zeros1461
Zeros (%)4.2%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:12.529972image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q15.75
median11.5
Q317.25
95-th percentile22
Maximum23
Range23
Interquartile range (IQR)11.5

Descriptive statistics

Standard deviation6.9222853
Coefficient of variation (CV)0.60193785
Kurtosis-1.2041745
Mean11.5
Median Absolute Deviation (MAD)6
Skewness0
Sum403236
Variance47.918033
MonotonicityNot monotonic
2024-03-08T12:15:12.683977image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 1461
 
4.2%
1 1461
 
4.2%
22 1461
 
4.2%
21 1461
 
4.2%
20 1461
 
4.2%
19 1461
 
4.2%
18 1461
 
4.2%
17 1461
 
4.2%
16 1461
 
4.2%
15 1461
 
4.2%
Other values (14) 20454
58.3%
ValueCountFrequency (%)
0 1461
4.2%
1 1461
4.2%
2 1461
4.2%
3 1461
4.2%
4 1461
4.2%
5 1461
4.2%
6 1461
4.2%
7 1461
4.2%
8 1461
4.2%
9 1461
4.2%
ValueCountFrequency (%)
23 1461
4.2%
22 1461
4.2%
21 1461
4.2%
20 1461
4.2%
19 1461
4.2%
18 1461
4.2%
17 1461
4.2%
16 1461
4.2%
15 1461
4.2%
14 1461
4.2%

PM2.5
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct567
Distinct (%)1.7%
Missing913
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean79.491602
Minimum2
Maximum941
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:12.895231image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile6
Q119
median55
Q3112
95-th percentile242
Maximum941
Range939
Interquartile range (IQR)93

Descriptive statistics

Standard deviation81.231739
Coefficient of variation (CV)1.0218908
Kurtosis6.1756316
Mean79.491602
Median Absolute Deviation (MAD)40
Skewness2.0281742
Sum2714717.7
Variance6598.5955
MonotonicityNot monotonic
2024-03-08T12:15:13.120700image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 824
 
2.3%
8 637
 
1.8%
10 624
 
1.8%
9 591
 
1.7%
7 577
 
1.6%
12 552
 
1.6%
11 536
 
1.5%
14 530
 
1.5%
6 528
 
1.5%
13 510
 
1.5%
Other values (557) 28242
80.5%
(Missing) 913
 
2.6%
ValueCountFrequency (%)
2 5
 
< 0.1%
3 824
2.3%
4 329
 
0.9%
4.6 1
 
< 0.1%
5 410
1.2%
6 528
1.5%
7 577
1.6%
8 637
1.8%
9 591
1.7%
10 624
1.8%
ValueCountFrequency (%)
941 1
< 0.1%
816 1
< 0.1%
762 1
< 0.1%
707 1
< 0.1%
689 1
< 0.1%
684 1
< 0.1%
671 1
< 0.1%
660 1
< 0.1%
650 1
< 0.1%
644 1
< 0.1%

PM10
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct605
Distinct (%)1.8%
Missing548
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean98.737026
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:13.312417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q131
median77
Q3138
95-th percentile268
Maximum999
Range997
Interquartile range (IQR)107

Descriptive statistics

Standard deviation89.143718
Coefficient of variation (CV)0.90283981
Kurtosis6.7058535
Mean98.737026
Median Absolute Deviation (MAD)51
Skewness1.9191421
Sum3408007.2
Variance7946.6025
MonotonicityNot monotonic
2024-03-08T12:15:13.503647image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 648
 
1.8%
5 431
 
1.2%
14 394
 
1.1%
13 383
 
1.1%
12 372
 
1.1%
9 360
 
1.0%
11 347
 
1.0%
10 347
 
1.0%
17 344
 
1.0%
18 343
 
1.0%
Other values (595) 30547
87.1%
(Missing) 548
 
1.6%
ValueCountFrequency (%)
2 8
 
< 0.1%
3 44
 
0.1%
4 26
 
0.1%
5 431
1.2%
6 648
1.8%
7 290
0.8%
8 334
1.0%
9 360
1.0%
10 347
1.0%
11 347
1.0%
ValueCountFrequency (%)
999 1
< 0.1%
951 1
< 0.1%
920 1
< 0.1%
917 1
< 0.1%
914 1
< 0.1%
912 1
< 0.1%
903 1
< 0.1%
873 1
< 0.1%
857 2
< 0.1%
835 1
< 0.1%

SO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct267
Distinct (%)0.8%
Missing1296
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean13.572039
Minimum0.2856
Maximum239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:13.760927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2856
5-th percentile2
Q12
median5
Q317
95-th percentile54
Maximum239
Range238.7144
Interquartile range (IQR)15

Descriptive statistics

Standard deviation19.572068
Coefficient of variation (CV)1.4420876
Kurtosis12.097853
Mean13.572039
Median Absolute Deviation (MAD)3
Skewness2.952752
Sum458300.6
Variance383.06585
MonotonicityNot monotonic
2024-03-08T12:15:14.023683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 10335
29.5%
3 3202
 
9.1%
4 1689
 
4.8%
5 1272
 
3.6%
6 1122
 
3.2%
7 994
 
2.8%
8 870
 
2.5%
1 789
 
2.3%
9 746
 
2.1%
10 723
 
2.1%
Other values (257) 12026
34.3%
(Missing) 1296
 
3.7%
ValueCountFrequency (%)
0.2856 18
 
0.1%
0.5712 16
 
< 0.1%
0.8568 9
 
< 0.1%
1 789
 
2.3%
1.1424 17
 
< 0.1%
1.428 16
 
< 0.1%
1.7136 12
 
< 0.1%
1.9992 8
 
< 0.1%
2 10335
29.5%
2.2 2
 
< 0.1%
ValueCountFrequency (%)
239 1
< 0.1%
207 1
< 0.1%
203 2
< 0.1%
192 1
< 0.1%
191 1
< 0.1%
188 1
< 0.1%
186 1
< 0.1%
182 1
< 0.1%
180 1
< 0.1%
177 1
< 0.1%

NO2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct517
Distinct (%)1.5%
Missing1365
Missing (%)3.9%
Infinite0
Infinite (%)0.0%
Mean43.908865
Minimum2
Maximum258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:14.228603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile8
Q119
median37
Q362
95-th percentile104
Maximum258
Range256
Interquartile range (IQR)43

Descriptive statistics

Standard deviation30.996828
Coefficient of variation (CV)0.70593553
Kurtosis1.1842668
Mean43.908865
Median Absolute Deviation (MAD)20
Skewness1.0766922
Sum1479684.8
Variance960.80332
MonotonicityNot monotonic
2024-03-08T12:15:14.507132image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 640
 
1.8%
13 579
 
1.7%
11 573
 
1.6%
12 571
 
1.6%
9 557
 
1.6%
22 553
 
1.6%
16 547
 
1.6%
14 535
 
1.5%
24 531
 
1.5%
21 527
 
1.5%
Other values (507) 28086
80.1%
(Missing) 1365
 
3.9%
ValueCountFrequency (%)
2 226
0.6%
2.6689 1
 
< 0.1%
2.8742 1
 
< 0.1%
3 114
0.3%
3.0795 3
 
< 0.1%
3.2848 1
 
< 0.1%
3.4901 1
 
< 0.1%
4 219
0.6%
4.3113 2
 
< 0.1%
4.5166 1
 
< 0.1%
ValueCountFrequency (%)
258 1
 
< 0.1%
241 1
 
< 0.1%
238 1
 
< 0.1%
214 1
 
< 0.1%
207 1
 
< 0.1%
204 3
< 0.1%
203 1
 
< 0.1%
196 1
 
< 0.1%
195 2
< 0.1%
194 1
 
< 0.1%

CO
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct115
Distinct (%)0.3%
Missing2178
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean1187.064
Minimum100
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:14.799547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile200
Q1400
median800
Q31500
95-th percentile3400
Maximum10000
Range9900
Interquartile range (IQR)1100

Descriptive statistics

Standard deviation1156.3741
Coefficient of variation (CV)0.9741464
Kurtosis9.7869587
Mean1187.064
Median Absolute Deviation (MAD)500
Skewness2.595075
Sum39037786
Variance1337201.1
MonotonicityNot monotonic
2024-03-08T12:15:14.991271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 2881
 
8.2%
400 2563
 
7.3%
200 2282
 
6.5%
500 2239
 
6.4%
600 2135
 
6.1%
700 1891
 
5.4%
800 1723
 
4.9%
900 1617
 
4.6%
1000 1448
 
4.1%
1100 1248
 
3.6%
Other values (105) 12859
36.7%
(Missing) 2178
 
6.2%
ValueCountFrequency (%)
100 948
 
2.7%
200 2282
6.5%
300 2881
8.2%
400 2563
7.3%
500 2239
6.4%
600 2135
6.1%
700 1891
5.4%
800 1723
4.9%
900 1617
4.6%
1000 1448
4.1%
ValueCountFrequency (%)
10000 7
< 0.1%
9900 4
< 0.1%
9800 1
 
< 0.1%
9700 3
< 0.1%
9600 4
< 0.1%
9500 2
 
< 0.1%
9400 2
 
< 0.1%
9300 3
< 0.1%
9200 2
 
< 0.1%
9100 2
 
< 0.1%

O3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct961
Distinct (%)2.9%
Missing1489
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean55.201321
Minimum0.2142
Maximum351.7164
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:15.216152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.2142
5-th percentile2
Q110
median43
Q377
95-th percentile173
Maximum351.7164
Range351.5022
Interquartile range (IQR)67

Descriptive statistics

Standard deviation54.873726
Coefficient of variation (CV)0.99406545
Kurtosis2.5804779
Mean55.201321
Median Absolute Deviation (MAD)33
Skewness1.5255511
Sum1853384.3
Variance3011.1258
MonotonicityNot monotonic
2024-03-08T12:15:15.716053image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 3478
 
9.9%
4 774
 
2.2%
3 714
 
2.0%
5 611
 
1.7%
6 550
 
1.6%
8 434
 
1.2%
7 430
 
1.2%
1 372
 
1.1%
9 371
 
1.1%
10 361
 
1.0%
Other values (951) 25480
72.7%
(Missing) 1489
 
4.2%
ValueCountFrequency (%)
0.2142 30
 
0.1%
0.4284 14
 
< 0.1%
0.6426 17
 
< 0.1%
0.8568 27
 
0.1%
1 372
1.1%
1.071 16
 
< 0.1%
1.2852 21
 
0.1%
1.4994 15
 
< 0.1%
1.7136 19
 
0.1%
1.9278 25
 
0.1%
ValueCountFrequency (%)
351.7164 1
< 0.1%
340 1
< 0.1%
339 2
< 0.1%
336 1
< 0.1%
333 1
< 0.1%
330 1
< 0.1%
329 1
< 0.1%
328 1
< 0.1%
326 2
< 0.1%
324 2
< 0.1%

TEMP
Real number (ℝ)

HIGH CORRELATION 

Distinct985
Distinct (%)2.8%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean13.387969
Minimum-16.8
Maximum40.6
Zeros199
Zeros (%)0.6%
Negative5679
Negative (%)16.2%
Memory size274.1 KiB
2024-03-08T12:15:15.931528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-16.8
5-th percentile-4.4
Q13
median14.4
Q323.2
95-th percentile30.5
Maximum40.6
Range57.4
Interquartile range (IQR)20.2

Descriptive statistics

Standard deviation11.483588
Coefficient of variation (CV)0.85775428
Kurtosis-1.1689039
Mean13.387969
Median Absolute Deviation (MAD)9.9
Skewness-0.1049148
Sum468752.96
Variance131.87279
MonotonicityNot monotonic
2024-03-08T12:15:16.178925image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 227
 
0.6%
-2 213
 
0.6%
1 203
 
0.6%
0 199
 
0.6%
-1 195
 
0.6%
2 184
 
0.5%
-4 167
 
0.5%
-5 149
 
0.4%
23.6 140
 
0.4%
23.8 136
 
0.4%
Other values (975) 33200
94.7%
ValueCountFrequency (%)
-16.8 1
 
< 0.1%
-16.7 1
 
< 0.1%
-16.2 1
 
< 0.1%
-16 2
< 0.1%
-15.9 2
< 0.1%
-15.8 4
< 0.1%
-15.7 1
 
< 0.1%
-15.4 1
 
< 0.1%
-15.3 1
 
< 0.1%
-15.1 2
< 0.1%
ValueCountFrequency (%)
40.6 1
< 0.1%
40.5 1
< 0.1%
40.2 1
< 0.1%
39.8 1
< 0.1%
39.2 1
< 0.1%
39 1
< 0.1%
38.6 1
< 0.1%
38.3 2
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%

PRES
Real number (ℝ)

HIGH CORRELATION 

Distinct602
Distinct (%)1.7%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1013.0619
Minimum988
Maximum1042.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:16.433485image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum988
5-th percentile997.7
Q11004.7
median1012.7
Q31021
95-th percentile1030
Maximum1042.8
Range54.8
Interquartile range (IQR)16.3

Descriptive statistics

Standard deviation10.177339
Coefficient of variation (CV)0.010046117
Kurtosis-0.8427892
Mean1013.0619
Median Absolute Deviation (MAD)8.2
Skewness0.13322236
Sum35470338
Variance103.57823
MonotonicityNot monotonic
2024-03-08T12:15:16.606775image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1015 270
 
0.8%
1017 259
 
0.7%
1014 253
 
0.7%
1018 251
 
0.7%
1013 244
 
0.7%
1020 244
 
0.7%
1019 238
 
0.7%
1016 234
 
0.7%
1012 229
 
0.7%
1021 216
 
0.6%
Other values (592) 32575
92.9%
ValueCountFrequency (%)
988 1
 
< 0.1%
988.4 2
< 0.1%
988.5 1
 
< 0.1%
988.6 1
 
< 0.1%
988.7 1
 
< 0.1%
988.8 2
< 0.1%
988.9 1
 
< 0.1%
989 1
 
< 0.1%
989.2 2
< 0.1%
989.3 4
< 0.1%
ValueCountFrequency (%)
1042.8 2
 
< 0.1%
1042.4 1
 
< 0.1%
1042.3 2
 
< 0.1%
1042.2 1
 
< 0.1%
1042 5
< 0.1%
1041.8 2
 
< 0.1%
1041.7 1
 
< 0.1%
1041.6 1
 
< 0.1%
1041.5 2
 
< 0.1%
1041.4 2
 
< 0.1%

DEWP
Real number (ℝ)

HIGH CORRELATION 

Distinct608
Distinct (%)1.7%
Missing54
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2.4650357
Minimum-36
Maximum27.5
Zeros82
Zeros (%)0.2%
Negative15486
Negative (%)44.2%
Memory size274.1 KiB
2024-03-08T12:15:16.924963image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-36
5-th percentile-20
Q1-8.8
median3.1
Q315.1
95-th percentile21.9
Maximum27.5
Range63.5
Interquartile range (IQR)23.9

Descriptive statistics

Standard deviation13.726622
Coefficient of variation (CV)5.5685287
Kurtosis-1.1267046
Mean2.4650357
Median Absolute Deviation (MAD)12
Skewness-0.19952933
Sum86300.9
Variance188.42015
MonotonicityNot monotonic
2024-03-08T12:15:17.248383image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17.6 157
 
0.4%
16.2 139
 
0.4%
16.8 139
 
0.4%
17.7 138
 
0.4%
17.5 137
 
0.4%
17.4 137
 
0.4%
17.3 134
 
0.4%
16 133
 
0.4%
15.8 131
 
0.4%
16.3 131
 
0.4%
Other values (598) 33634
95.9%
ValueCountFrequency (%)
-36 1
< 0.1%
-35.7 1
< 0.1%
-35.5 1
< 0.1%
-35.3 1
< 0.1%
-35.1 1
< 0.1%
-34.8 1
< 0.1%
-34.6 1
< 0.1%
-34.4 1
< 0.1%
-34.3 1
< 0.1%
-34 1
< 0.1%
ValueCountFrequency (%)
27.5 1
 
< 0.1%
27.4 2
 
< 0.1%
27.3 1
 
< 0.1%
27.2 1
 
< 0.1%
27.1 2
 
< 0.1%
27 3
< 0.1%
26.9 7
< 0.1%
26.8 7
< 0.1%
26.7 6
< 0.1%
26.6 6
< 0.1%

RAIN
Real number (ℝ)

SKEWED  ZEROS 

Distinct120
Distinct (%)0.3%
Missing51
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.061094451
Minimum0
Maximum37.3
Zeros33702
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:17.522152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum37.3
Range37.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.76166849
Coefficient of variation (CV)12.467065
Kurtosis825.33696
Mean0.061094451
Median Absolute Deviation (MAD)0
Skewness25.139809
Sum2139.1
Variance0.58013889
MonotonicityNot monotonic
2024-03-08T12:15:17.835058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 33702
96.1%
0.1 302
 
0.9%
0.2 143
 
0.4%
0.3 101
 
0.3%
0.4 84
 
0.2%
0.5 63
 
0.2%
0.6 57
 
0.2%
0.7 50
 
0.1%
0.8 39
 
0.1%
0.9 35
 
0.1%
Other values (110) 437
 
1.2%
(Missing) 51
 
0.1%
ValueCountFrequency (%)
0 33702
96.1%
0.1 302
 
0.9%
0.2 143
 
0.4%
0.3 101
 
0.3%
0.4 84
 
0.2%
0.5 63
 
0.2%
0.6 57
 
0.2%
0.7 50
 
0.1%
0.8 39
 
0.1%
0.9 35
 
0.1%
ValueCountFrequency (%)
37.3 1
< 0.1%
36.9 1
< 0.1%
31.2 1
< 0.1%
30.9 1
< 0.1%
27 1
< 0.1%
25.3 1
< 0.1%
24.7 1
< 0.1%
24 1
< 0.1%
23.2 1
< 0.1%
22.5 1
< 0.1%

wd
Categorical

MISSING 

Distinct16
Distinct (%)< 0.1%
Missing483
Missing (%)1.4%
Memory size274.1 KiB
NNE
4540 
N
3877 
NE
3351 
SSE
2963 
SE
2665 
Other values (11)
17185 

Length

Max length3
Median length2
Mean length2.2170556
Min length1

Characters and Unicode

Total characters76668
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNW
2nd rowNW
3rd rowNW
4th rowNW
5th rowNW

Common Values

ValueCountFrequency (%)
NNE 4540
12.9%
N 3877
11.1%
NE 3351
9.6%
SSE 2963
8.5%
SE 2665
 
7.6%
S 2613
 
7.5%
NW 2446
 
7.0%
WNW 2051
 
5.8%
NNW 1656
 
4.7%
SSW 1586
 
4.5%
Other values (6) 6833
19.5%

Length

2024-03-08T12:15:18.093688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nne 4540
13.1%
n 3877
11.2%
ne 3351
9.7%
sse 2963
8.6%
se 2665
 
7.7%
s 2613
 
7.6%
nw 2446
 
7.1%
wnw 2051
 
5.9%
nnw 1656
 
4.8%
ssw 1586
 
4.6%
Other values (6) 6833
19.8%

Most occurring characters

ValueCountFrequency (%)
N 25533
33.3%
E 20258
26.4%
S 17463
22.8%
W 13414
17.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 76668
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 25533
33.3%
E 20258
26.4%
S 17463
22.8%
W 13414
17.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 76668
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 25533
33.3%
E 20258
26.4%
S 17463
22.8%
W 13414
17.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76668
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 25533
33.3%
E 20258
26.4%
S 17463
22.8%
W 13414
17.5%

WSPM
Real number (ℝ)

ZEROS 

Distinct103
Distinct (%)0.3%
Missing44
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.8075328
Minimum0
Maximum12.8
Zeros792
Zeros (%)2.3%
Negative0
Negative (%)0.0%
Memory size274.1 KiB
2024-03-08T12:15:18.393125image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q11
median1.5
Q32.3
95-th percentile4.4
Maximum12.8
Range12.8
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.287817
Coefficient of variation (CV)0.71247226
Kurtosis4.3351173
Mean1.8075328
Median Absolute Deviation (MAD)0.6
Skewness1.7606259
Sum63299.8
Variance1.6584726
MonotonicityNot monotonic
2024-03-08T12:15:18.752435image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1 1975
 
5.6%
1.3 1915
 
5.5%
1.2 1891
 
5.4%
1 1831
 
5.2%
1.4 1682
 
4.8%
1.5 1599
 
4.6%
0.9 1574
 
4.5%
1.6 1511
 
4.3%
0.8 1357
 
3.9%
1.7 1354
 
3.9%
Other values (93) 18331
52.3%
ValueCountFrequency (%)
0 792
2.3%
0.1 372
 
1.1%
0.2 353
 
1.0%
0.3 211
 
0.6%
0.4 512
 
1.5%
0.5 651
1.9%
0.6 882
2.5%
0.7 1116
3.2%
0.8 1357
3.9%
0.9 1574
4.5%
ValueCountFrequency (%)
12.8 1
 
< 0.1%
11 1
 
< 0.1%
10.9 1
 
< 0.1%
10.7 1
 
< 0.1%
10.1 1
 
< 0.1%
9.9 1
 
< 0.1%
9.7 3
< 0.1%
9.6 1
 
< 0.1%
9.5 1
 
< 0.1%
9.4 3
< 0.1%

station
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size274.1 KiB
Shunyi
35064 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters210384
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShunyi
2nd rowShunyi
3rd rowShunyi
4th rowShunyi
5th rowShunyi

Common Values

ValueCountFrequency (%)
Shunyi 35064
100.0%

Length

2024-03-08T12:15:19.061512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-08T12:15:19.204076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
shunyi 35064
100.0%

Most occurring characters

ValueCountFrequency (%)
S 35064
16.7%
h 35064
16.7%
u 35064
16.7%
n 35064
16.7%
y 35064
16.7%
i 35064
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 175320
83.3%
Uppercase Letter 35064
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
h 35064
20.0%
u 35064
20.0%
n 35064
20.0%
y 35064
20.0%
i 35064
20.0%
Uppercase Letter
ValueCountFrequency (%)
S 35064
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 210384
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 35064
16.7%
h 35064
16.7%
u 35064
16.7%
n 35064
16.7%
y 35064
16.7%
i 35064
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
S 35064
16.7%
h 35064
16.7%
u 35064
16.7%
n 35064
16.7%
y 35064
16.7%
i 35064
16.7%

Interactions

2024-03-08T12:15:07.027833image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:30.395834image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.328261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.796845image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:38.200518image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:40.956580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:44.868457image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.628392image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.919002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:52.288256image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.898873image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.062060image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:59.084314image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.102812image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.673513image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.222311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:30.558234image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.486720image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.953355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:38.418713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:41.164004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:45.058203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.800206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.049277image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:52.497639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.047429image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.194211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:59.238514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.330685image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.811974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.398163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:30.730020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.669268image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.083051image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:38.608473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:41.347149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:45.251946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.930843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.222378image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:52.950456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.180279image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.325456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:59.425636image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.492269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.968890image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.570695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:30.898272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.841216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.272206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:38.790524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:41.570604image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:45.458800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.062349image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.379764image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.111747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.308624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.461103image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:59.691870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.680497image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.133415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.682375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.050707image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.998537image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.422031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.017534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:41.775779image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:45.612437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.212722image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.511676image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.255883image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.424142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.586705image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:59.870412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.828336image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.283673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.810283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.200375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.125338image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.566823image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.189086image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:41.982384image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:45.858593image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.363592image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.642322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.396548image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.543058image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.707717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.036372image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:02.975110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.439900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:07.957437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.345547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.279389image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.719875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.318142image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:42.180768image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.051591image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.530993image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.769088image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.518954image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.663693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.866153image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.178793image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:03.127702image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.598247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.119242image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.563183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.451236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:36.911264image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.474304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:43.010542image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.289899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.703124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:50.922407image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.647928image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.819216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:57.993466image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.323968image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:03.325911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.742422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.305159image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.745229image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.589001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.073225image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.612271image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:43.204426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.483494image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.827887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.053584image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.844149image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:55.948615image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.124030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.459699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:03.470000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:05.926163image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.452183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:31.913196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.741050image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.201177image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.815196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:43.413998image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.645133image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:48.968409image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.189403image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:53.992370image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.087855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.259094image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.600692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:03.673769image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.085957image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.597326image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:32.181245image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:34.914014image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.351068image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:39.999822image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:43.600398image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.851037image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.153824image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.425448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.152905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.219253image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.389961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.736657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:03.867648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.263659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.727877image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:32.358747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.145258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.506055image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:40.169864image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:43.874259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:46.981923image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.292575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.645937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.318298image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.399081image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.510839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:00.914340image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.027514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.395247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:08.874169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:32.849442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.319305image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.667763image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:40.374152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:44.105186image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.195839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.432749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.811783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.474109image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.587660image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.655200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:01.603540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.187887image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.561687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:09.051984image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.013269image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.479255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.817188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:40.594104image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:44.382046image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.371711image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.591374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:51.992911image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.605004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.747246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.806999image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:01.760209image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.343699image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.726555image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:09.175363image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:33.173880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:35.617002image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:37.996453image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:40.761130image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:44.598783image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:47.498152image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:49.756677image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:52.126096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:54.746423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:56.902247image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:14:58.971808image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:01.944025image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:04.507482image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-03-08T12:15:06.870589image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-03-08T12:15:19.376687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
CODEWPNO2NoO3PM10PM2.5PRESRAINSO2TEMPWSPMdayhourmonthwdyear
CO1.0000.1070.717-0.080-0.3870.7400.8260.0810.0130.562-0.202-0.3760.010-0.0120.0460.0740.098
DEWP0.1071.000-0.062-0.1020.2210.1780.253-0.7690.175-0.3280.821-0.1610.021-0.0130.2500.1210.152
NO20.717-0.0621.0000.013-0.5700.6820.6700.154-0.0960.553-0.286-0.4470.0370.0630.0080.0720.070
No-0.080-0.1020.0131.000-0.067-0.077-0.0580.1710.003-0.125-0.122-0.0030.0180.0010.0440.1150.862
O3-0.3870.221-0.570-0.0671.000-0.235-0.255-0.4000.008-0.1810.5510.4530.0040.284-0.1710.1490.057
PM100.7400.1780.682-0.077-0.2351.0000.914-0.098-0.0680.4830.001-0.2740.0340.063-0.0370.0780.068
PM2.50.8260.2530.670-0.058-0.2550.9141.000-0.091-0.0160.485-0.008-0.3290.0150.006-0.0270.0660.054
PRES0.081-0.7690.1540.171-0.400-0.098-0.0911.000-0.0860.264-0.831-0.0420.019-0.038-0.0370.0880.153
RAIN0.0130.175-0.0960.0030.008-0.068-0.016-0.0861.000-0.1250.0440.016-0.004-0.0060.0410.0110.000
SO20.562-0.3280.553-0.125-0.1810.4830.4850.264-0.1251.000-0.348-0.1170.0030.071-0.1990.0450.091
TEMP-0.2020.821-0.286-0.1220.5510.001-0.008-0.8310.044-0.3481.0000.1570.0170.1450.1230.1270.148
WSPM-0.376-0.161-0.447-0.0030.453-0.274-0.329-0.0420.016-0.1170.1571.000-0.0090.122-0.1390.1530.033
day0.0100.0210.0370.0180.0040.0340.0150.019-0.0040.0030.017-0.0091.0000.0000.0100.0320.000
hour-0.012-0.0130.0630.0010.2840.0630.006-0.038-0.0060.0710.1450.1220.0001.0000.0000.1340.000
month0.0460.2500.0080.044-0.171-0.037-0.027-0.0370.041-0.1990.123-0.1390.0100.0001.0000.1010.249
wd0.0740.1210.0720.1150.1490.0780.0660.0880.0110.0450.1270.1530.0320.1340.1011.0000.117
year0.0980.1520.0700.8620.0570.0680.0540.1530.0000.0910.1480.0330.0000.0000.2490.1171.000

Missing values

2024-03-08T12:15:09.412266image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-08T12:15:10.060106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-08T12:15:10.503951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
0120133103.06.03.08.0300.044.0-0.91025.8-20.50.0NW9.3Shunyi
12201331112.012.03.07.0300.047.0-1.11026.1-21.30.0NW9.4Shunyi
23201331214.014.0NaN7.0200.022.0-1.71026.2-23.00.0NW8.6Shunyi
34201331312.012.03.05.0NaNNaN-2.11027.3-23.30.0NW6.6Shunyi
45201331412.012.03.0NaN200.011.0-2.41027.7-22.90.0NW4.5Shunyi
56201331511.011.03.07.0200.045.0-2.81028.2-22.10.0NNW1.7Shunyi
67201331612.012.03.09.0300.074.0-4.01029.0-21.20.0NNE1.6Shunyi
78201331713.013.03.023.0300.059.0-2.41030.5-21.30.0NE1.7Shunyi
8920133188.08.03.019.0400.066.0-1.01031.2-21.80.0NNW2.7Shunyi
91020133193.06.03.021.0400.060.00.01031.3-22.90.0SSW0.8Shunyi
NoyearmonthdayhourPM2.5PM10SO2NO2COO3TEMPPRESDEWPRAINwdWSPMstation
35054350552017228146.014.02.07.0200.096.014.51013.9-12.70.0NW5.1Shunyi
35055350562017228156.014.02.08.0200.097.015.31013.2-13.00.0WNW4.2Shunyi
350563505720172281610.010.02.07.0200.099.015.11012.9-13.20.0NW3.3Shunyi
350573505820172281711.026.03.014.0300.092.014.61013.0-13.60.0WNW3.2Shunyi
350583505920172281815.035.06.039.0500.067.012.61013.5-11.90.0WSW1.3Shunyi
350593506020172281927.072.08.092.0800.016.010.31014.2-12.40.0W1.8Shunyi
350603506120172282047.055.017.086.01100.019.09.81014.5-9.90.0NW1.5Shunyi
350613506220172282118.028.04.030.0500.064.09.11014.6-12.70.0NE1.7Shunyi
350623506320172282218.020.09.033.0500.059.07.11015.2-13.20.0WNW1.8Shunyi
350633506420172282315.022.013.034.0500.060.07.41014.9-11.90.0N1.4Shunyi